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Found 6 papers, 3 papers with code
Transferability Metrics for Object Detection
In our experiments, we compare TLogME to state-of-the-art metrics in the estimation of transfer performance of the Faster-RCNN object detector.
Towards Clear Expectations for Uncertainty Estimation
If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols.
Performance Prediction Under Dataset Shift
ML models deployed in production often have to face unknown domain changes, fundamentally different from their training settings.
Sample Noise Impact on Active Learning
This work explores the effect of noisy sample selection in active learning strategies.
Ensembling Shift Detectors: an Extensive Empirical Evaluation
The term dataset shift refers to the situation where the data used to train a machine learning model is different from where the model operates.
Rebuilding Trust in Active Learning with Actionable Metrics
Active Learning (AL) is an active domain of research, but is seldom used in the industry despite the pressing needs.
